By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
Welcome to the Coursera course, Industrial Internet of Things (IoT) on Google Cloud Platform (GCP) brought to you by the Google Cloud team. I’m Catherine Gamboa and I’m going to be your guide.
This course covers the entire Industrial IoT network architecture from sensors and devices to analysis. The course discusses sensors and devices but the focus is on the cloud side. You'll learn about the importance of scaling, device communication, and processing streaming data. The course uses simulated devices in the labs to allow you to concentrate on learning the cloud side of IIoT. The course is a little different than most Coursera courses because there is very little video. Most of the learning is done with short readings, quizzes, and labs.
This course takes about two weeks to complete, 11-12 hours of work with 6 of those hours spent in labs. By the end of this course, you’ll be able to: create a streaming data pipeline, to create registries with Cloud IoT Core, topics and subscriptions with Cloud Pub/Sub, store data on Google Cloud Storage, query the data in BigQuery, and gain data insights with Dataprep. You'll learn and practice these skills in 7 labs. Then you'll have an opportunity to test yourself in an optional capstone lab using simulated devices or Cloud IoT Core Inspector.

강사:

Google Cloud Training

스크립트

Welcome to industrial IoT on Google Cloud IoT platform. I'm Catherine Gamboa, and I'm a technical course developer at Google. This course is an introduction to IoT in general and Cloud IoT on Google Cloud Platform specifically. The course is divided into seven modules, and each of these modules covers a specific area of IoT architecture. The first module, foundations of GCP architecture defines what the Internet of Things is. The module begins with a discussion of the basic structure of an IoT network, followed by a discussion of how IoT is done on Google Cloud Platform. Second module, Sensors, Devices, and Cloud Communication is an overview of the types of sensors available for IoT. The module covers how to select a sensor, how to select a device, the role of devices, and standard IoT communication protocols. Since the focus of the course is on the Cloud side of IoT, things that are not on the Cloud, like actual sensors and devices are not used in this course. Instead, the labs use simulated devices and data. The third module, Google Cloud IoT Platform, covers the ingest and process stages of IoT architecture. Data ingesting and processing are accomplished using Google's fully integrated services known as Cloud IoT Core, Cloud/PubSub, Cloud Storage, and Cloud Dataflow. You do a mini lab for each of the services discussed in this module. This reinforces the concepts just learned, and allows you to practice using Google Cloud platform console. The fourth module, Creating Pipelines leads the student through a complete IoT on GCP data pipeline. You use simulated devices to create a pipeline, and store data on Cloud storage. The lab in this module uses all the skills you've learned in module three, combining each of the services into a fully integrated streaming data pipeline. In the fifth and sixth modules, you analyze streaming data with BigQuery and Cloud Dataproc. Once again, the labs in these modules let you practice your new skills. You create streaming data pipelines to analysis tools that enhance the value of your data. When you're done with this lab, you'll be able to find valuable insights in your IoT data. The final module is an optional capstone project. In this module, the basics for each of the GCP stages are quickly reviewed, and the students are asked to build a GCP IoT data pipeline with minimal assistance. Minimal assistance sounds a little scary, but don't worry. By this time in the course, you'll have had plenty of practice with these services. As I mentioned before, only simulated devices are used in the lab. This allows you the opportunity to focus on learning the Cloud side of the Internet of Things, which happens to be one of the goals of this course. Additional goals of this course include, becoming very familiar with the Google Cloud IoT services and analysis tools, and of course, learning to gain insights into your data. I think you might be excited to hear that there are seven labs in this course. The first four are mini labs. Each one focuses on a Google service. The fifth lab, you tie all the services together when you create a data streaming pipeline from simulated device to cloud storage. Lab six and seven change the pipeline from a storage destination to data analysis. As you gain insights into your data, you really start to discover its value. Lab eight is an optional challenge Lab. In this lab, you create a streaming data pipeline again. This time, with very little instruction. I think you will find this lab to be challenging, yet quite satisfying. This course is a little different from other Coursera courses you may have taken. There are a few videos throughout the course. But the majority of learning is done to short readings, quizzes, discussion prompts, and Labs. I hope you enjoy this activity focus format, and I look forward to seeing your work in the optional capstone project. I've included a video that is a good introduction to some of what you will be doing in this course. It's from a Google video series called Take five. This one is called IoT Core with Pub/Sub, Dataflow, and BigQuery. In five minutes, Jonathan and Stephanie are going to race through the setup of streaming data pipeline. As you watch, pay attention. Soon, you'll be just as proficient in the Google Cloud platform console.